Center for Computational Medicine
The University of Texas at Austin
Joint Mentorship: Dr. Vagheesh M. Narasimhan & Dr. Charley Taylor
The Center for Computational Medicine at The University of Texas at Austin invites applications for a Postdoctoral Fellow to lead innovative research at the interface of cardiovascular imaging, statistical genetics, and computational medicine. This position will interface with several units on campus, the Department of Integrative Biology, the Department of Statistics and Data Science, the Oden Institute for Computational Engineering and Sciences and the Department of Internal Medicine at Dell Medical School.
The successful candidate will analyze quantitative image-derived phenotypes from coronary CT angiography (CCTA) and integrate these traits with large-scale human genetic datasets comprising hundreds of thousands of individuals. The overarching goal is to elucidate the genetic architecture and biological mechanisms underlying atherosclerosis and coronary artery disease, and to advance risk stratification and therapeutic discovery.
This is a highly interdisciplinary and methodologically rigorous research position designed for candidates seeking to build an independent academic research trajectory in imaging-genetics and computational medicine.
Dr. Narasimhan’s lab develops methods at the intersection of human genetics and medical imaging. The lab focuses on:
●Integration of imaging-derived traits with genetic data
●Multimodal machine learning for biological discovery
●Translational genomics and risk modeling
The fellow will work in an environment that emphasizes methodological innovation, statistical rigor, reproducibility, and high-impact scholarship.
Dr. Taylor is a leader in computational medicine and cardiovascular modeling. His work has transformed noninvasive cardiac assessment through physics-based modeling and AI-driven quantification of coronary physiology. His research program focuses on:
●Image-based modeling of coronary anatomy and hemodynamics
●AI-integrated computational simulation (“digital twins”)
●Translation of computational methods into clinical cardiovascular practice
●Advancing precision cardiology through mechanistic modeling
The fellow will work with colleagues with expertise in CCTA phenotyping, coronary modeling, and translational cardiovascular science.
The fellow will:
●Help to develop and validate CCTA-derived quantitative phenotypes, including plaque burden, plaque composition, stenosis metrics, coronary morphology, and related structural features.
●Conduct large-scale genome-wide association studies (GWAS) of imaging-derived phenotypes.
●Examine single cell genetic data from coronary tissue
●Perform downstream analyses for:
○Fine-mapping and colocalization
○Rare variant and gene-based testing (as applicable)
○Polygenic risk modeling
○Genetic correlation and cross-trait analyses
○Mendelian randomization and causal inference
○Identifying cell types and programs associated with disease progression
●Importantly the fellow will integrate imaging, genetic, and clinical data to identify novel biological pathways and therapeutic targets.
●Lead manuscript preparation and contribute to competitive extramural funding proposals.
The fellow will be encouraged to develop independent research questions within this broader program.
This position offers:
●Close mentorship from leaders in computational cardiology and statistical genetics.
●Access to large-scale multimodal datasets and advanced computational resources.
●Opportunities to develop independent projects and first-author publications.
●Structured support for career development, including grant writing and academic presentation.
●Have access to the largest academic computing cluster in the world, including the largest GPU cluster.
●PhD (or equivalent) in statistical genetics, computational biology, biostatistics, biomedical engineering, computer science, epidemiology, or a related quantitative discipline.
●Demonstrated experience with large-scale human genetic data analysis (GWAS pipelines, QC, mixed models, population structure adjustment).
●Strong programming skills (e.g., Python, R) and experience working in Linux/HPC or cloud computing environments.
●Evidence of scholarly productivity (publications or substantial research contributions).
●Experience with medical imaging analysis or machine learning.
●Familiarity with cardiovascular imaging or coronary artery disease biology.
●Experience with biobank-scale datasets.
●Interest in developing independent grant proposals and pursuing an academic research career.
Applicants should submit:
Review of applications will begin immediately and continue until the position is filled.
A security sensitive background check will be conducted on the applicant selected.
Contact Information
For inquiries about the position, please contact vagheesh@utexas.edu.
For application questions, please contact Lynnlee Harrell at hr@oden.utexas.edu.
The University of Texas at Austin, as an equal opportunity/affirmative action employer, complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions.
